Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations113036
Missing cells0
Missing cells (%)0.0%
Duplicate rows993
Duplicate rows (%)0.9%
Total size in memory65.4 MiB
Average record size in memory607.0 B

Variable types

DateTime1
Numeric9
Categorical6
Text2

Alerts

Dataset has 993 (0.9%) duplicate rowsDuplicates
Age_Group is highly overall correlated with Customer_AgeHigh correlation
Cost is highly overall correlated with Profit and 3 other fieldsHigh correlation
Customer_Age is highly overall correlated with Age_GroupHigh correlation
Order_Quantity is highly overall correlated with Product_Category and 2 other fieldsHigh correlation
Product_Category is highly overall correlated with Order_Quantity and 3 other fieldsHigh correlation
Profit is highly overall correlated with Cost and 3 other fieldsHigh correlation
Revenue is highly overall correlated with Cost and 3 other fieldsHigh correlation
Sub_Category is highly overall correlated with Product_CategoryHigh correlation
Unit_Cost is highly overall correlated with Cost and 5 other fieldsHigh correlation
Unit_Price is highly overall correlated with Cost and 5 other fieldsHigh correlation

Reproduction

Analysis started2024-09-11 12:42:31.737981
Analysis finished2024-09-11 12:43:09.616436
Duration37.88 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Date
Date

Distinct1884
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size883.2 KiB
Minimum2011-01-01 00:00:00
Maximum2016-07-31 00:00:00
2024-09-11T18:13:09.946021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:10.439207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.665753
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:10.864713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7815668
Coefficient of variation (CV)0.56055825
Kurtosis-1.1900824
Mean15.665753
Median Absolute Deviation (MAD)8
Skewness0.013722013
Sum1770794
Variance77.115915
MonotonicityNot monotonic
2024-09-11T18:13:11.180309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 4078
 
3.6%
7 4072
 
3.6%
3 3998
 
3.5%
19 3996
 
3.5%
8 3986
 
3.5%
20 3986
 
3.5%
15 3968
 
3.5%
28 3938
 
3.5%
18 3830
 
3.4%
12 3826
 
3.4%
Other values (21) 73358
64.9%
ValueCountFrequency (%)
1 3780
3.3%
2 3554
3.1%
3 3998
3.5%
4 3530
3.1%
5 3582
3.2%
6 3818
3.4%
7 4072
3.6%
8 3986
3.5%
9 3630
3.2%
10 3486
3.1%
ValueCountFrequency (%)
31 2174
1.9%
30 3272
2.9%
29 3260
2.9%
28 3938
3.5%
27 3582
3.2%
26 3636
3.2%
25 3460
3.1%
24 4078
3.6%
23 3512
3.1%
22 3460
3.1%

Month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
June
11234 
December
11200 
May
11128 
April
10182 
March
9674 
Other values (7)
59618 

Length

Max length9
Median length7
Mean length6.0868042
Min length3

Characters and Unicode

Total characters688028
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowNovember
3rd rowMarch
4th rowMarch
5th rowMay

Common Values

ValueCountFrequency (%)
June 11234
9.9%
December 11200
9.9%
May 11128
9.8%
April 10182
9.0%
March 9674
8.6%
January 9284
8.2%
February 9022
8.0%
October 8750
7.7%
November 8734
7.7%
August 8200
7.3%
Other values (2) 15628
13.8%

Length

2024-09-11T18:13:11.576172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
june 11234
9.9%
december 11200
9.9%
may 11128
9.8%
april 10182
9.0%
march 9674
8.6%
january 9284
8.2%
february 9022
8.0%
october 8750
7.7%
november 8734
7.7%
august 8200
7.3%
Other values (2) 15628
13.8%

Most occurring characters

ValueCountFrequency (%)
e 104572
15.2%
r 84034
12.2%
u 53402
 
7.8%
a 48392
 
7.0%
b 45872
 
6.7%
y 36896
 
5.4%
c 29624
 
4.3%
m 28100
 
4.1%
J 27980
 
4.1%
t 25116
 
3.7%
Other values (16) 204040
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688028
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 104572
15.2%
r 84034
12.2%
u 53402
 
7.8%
a 48392
 
7.0%
b 45872
 
6.7%
y 36896
 
5.4%
c 29624
 
4.3%
m 28100
 
4.1%
J 27980
 
4.1%
t 25116
 
3.7%
Other values (16) 204040
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688028
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 104572
15.2%
r 84034
12.2%
u 53402
 
7.8%
a 48392
 
7.0%
b 45872
 
6.7%
y 36896
 
5.4%
c 29624
 
4.3%
m 28100
 
4.1%
J 27980
 
4.1%
t 25116
 
3.7%
Other values (16) 204040
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688028
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 104572
15.2%
r 84034
12.2%
u 53402
 
7.8%
a 48392
 
7.0%
b 45872
 
6.7%
y 36896
 
5.4%
c 29624
 
4.3%
m 28100
 
4.1%
J 27980
 
4.1%
t 25116
 
3.7%
Other values (16) 204040
29.7%

Year
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.4017
Minimum2011
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:11.893892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2013
Q12013
median2014
Q32016
95-th percentile2016
Maximum2016
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.2725104
Coefficient of variation (CV)0.00063170636
Kurtosis-0.51118635
Mean2014.4017
Median Absolute Deviation (MAD)1
Skewness-0.37112037
Sum2.2769992 × 108
Variance1.6192827
MonotonicityNot monotonic
2024-09-11T18:13:12.252355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2014 29398
26.0%
2016 29398
26.0%
2013 24443
21.6%
2015 24443
21.6%
2012 2677
 
2.4%
2011 2677
 
2.4%
ValueCountFrequency (%)
2011 2677
 
2.4%
2012 2677
 
2.4%
2013 24443
21.6%
2014 29398
26.0%
2015 24443
21.6%
2016 29398
26.0%
ValueCountFrequency (%)
2016 29398
26.0%
2015 24443
21.6%
2014 29398
26.0%
2013 24443
21.6%
2012 2677
 
2.4%
2011 2677
 
2.4%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.919212
Minimum17
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:12.627602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q128
median35
Q343
95-th percentile56
Maximum87
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.021936
Coefficient of variation (CV)0.3068535
Kurtosis-0.1188735
Mean35.919212
Median Absolute Deviation (MAD)8
Skewness0.52530027
Sum4060164
Variance121.48306
MonotonicityNot monotonic
2024-09-11T18:13:13.040978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 4382
 
3.9%
34 4300
 
3.8%
29 4214
 
3.7%
32 4092
 
3.6%
28 3988
 
3.5%
35 3968
 
3.5%
33 3958
 
3.5%
30 3836
 
3.4%
40 3604
 
3.2%
37 3540
 
3.1%
Other values (60) 73154
64.7%
ValueCountFrequency (%)
17 1306
 
1.2%
18 1760
1.6%
19 2010
1.8%
20 2020
1.8%
21 2230
2.0%
22 2636
2.3%
23 2826
2.5%
24 3040
2.7%
25 3050
2.7%
26 3352
3.0%
ValueCountFrequency (%)
87 6
 
< 0.1%
86 8
 
< 0.1%
85 16
< 0.1%
84 18
< 0.1%
82 4
 
< 0.1%
81 12
< 0.1%
80 6
 
< 0.1%
79 10
< 0.1%
78 22
< 0.1%
77 16
< 0.1%

Age_Group
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Adults (35-64)
55824 
Young Adults (25-34)
38654 
Youth (<25)
17828 
Seniors (64+)
 
730

Length

Max length20
Median length14
Mean length15.572154
Min length11

Characters and Unicode

Total characters1760214
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYouth (<25)
2nd rowYouth (<25)
3rd rowAdults (35-64)
4th rowAdults (35-64)
5th rowAdults (35-64)

Common Values

ValueCountFrequency (%)
Adults (35-64) 55824
49.4%
Young Adults (25-34) 38654
34.2%
Youth (<25) 17828
 
15.8%
Seniors (64+) 730
 
0.6%

Length

2024-09-11T18:13:13.336001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T18:13:13.640776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
adults 94478
35.7%
35-64 55824
21.1%
young 38654
14.6%
25-34 38654
14.6%
youth 17828
 
6.7%
25 17828
 
6.7%
seniors 730
 
0.3%
64 730
 
0.3%

Most occurring characters

ValueCountFrequency (%)
151690
 
8.6%
u 150960
 
8.6%
) 113036
 
6.4%
( 113036
 
6.4%
t 112306
 
6.4%
5 112306
 
6.4%
s 95208
 
5.4%
4 95208
 
5.4%
- 94478
 
5.4%
d 94478
 
5.4%
Other values (16) 627508
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1760214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
151690
 
8.6%
u 150960
 
8.6%
) 113036
 
6.4%
( 113036
 
6.4%
t 112306
 
6.4%
5 112306
 
6.4%
s 95208
 
5.4%
4 95208
 
5.4%
- 94478
 
5.4%
d 94478
 
5.4%
Other values (16) 627508
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1760214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
151690
 
8.6%
u 150960
 
8.6%
) 113036
 
6.4%
( 113036
 
6.4%
t 112306
 
6.4%
5 112306
 
6.4%
s 95208
 
5.4%
4 95208
 
5.4%
- 94478
 
5.4%
d 94478
 
5.4%
Other values (16) 627508
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1760214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
151690
 
8.6%
u 150960
 
8.6%
) 113036
 
6.4%
( 113036
 
6.4%
t 112306
 
6.4%
5 112306
 
6.4%
s 95208
 
5.4%
4 95208
 
5.4%
- 94478
 
5.4%
d 94478
 
5.4%
Other values (16) 627508
35.6%

Customer_Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
M
58312 
F
54724 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113036
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 58312
51.6%
F 54724
48.4%

Length

2024-09-11T18:13:13.988231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T18:13:14.259708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
m 58312
51.6%
f 54724
48.4%

Most occurring characters

ValueCountFrequency (%)
M 58312
51.6%
F 54724
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 58312
51.6%
F 54724
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 58312
51.6%
F 54724
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 58312
51.6%
F 54724
48.4%

Country
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
United States
39206 
Australia
23936 
Canada
14178 
United Kingdom
13620 
Germany
11098 

Length

Max length14
Median length13
Mean length10.125305
Min length6

Characters and Unicode

Total characters1144524
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanada
2nd rowCanada
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
United States 39206
34.7%
Australia 23936
21.2%
Canada 14178
 
12.5%
United Kingdom 13620
 
12.0%
Germany 11098
 
9.8%
France 10998
 
9.7%

Length

2024-09-11T18:13:14.597371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T18:13:14.967532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
united 52826
31.8%
states 39206
23.6%
australia 23936
14.4%
canada 14178
 
8.5%
kingdom 13620
 
8.2%
germany 11098
 
6.7%
france 10998
 
6.6%

Most occurring characters

ValueCountFrequency (%)
t 155174
13.6%
a 151708
13.3%
e 114128
10.0%
n 102720
9.0%
i 90382
 
7.9%
d 80624
 
7.0%
s 63142
 
5.5%
U 52826
 
4.6%
52826
 
4.6%
r 46032
 
4.0%
Other values (13) 234962
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1144524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 155174
13.6%
a 151708
13.3%
e 114128
10.0%
n 102720
9.0%
i 90382
 
7.9%
d 80624
 
7.0%
s 63142
 
5.5%
U 52826
 
4.6%
52826
 
4.6%
r 46032
 
4.0%
Other values (13) 234962
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1144524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 155174
13.6%
a 151708
13.3%
e 114128
10.0%
n 102720
9.0%
i 90382
 
7.9%
d 80624
 
7.0%
s 63142
 
5.5%
U 52826
 
4.6%
52826
 
4.6%
r 46032
 
4.0%
Other values (13) 234962
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1144524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 155174
13.6%
a 151708
13.3%
e 114128
10.0%
n 102720
9.0%
i 90382
 
7.9%
d 80624
 
7.0%
s 63142
 
5.5%
U 52826
 
4.6%
52826
 
4.6%
r 46032
 
4.0%
Other values (13) 234962
20.5%

State
Text

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
2024-09-11T18:13:15.439390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.645387
Min length4

Characters and Unicode

Total characters1203312
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBritish Columbia
2nd rowBritish Columbia
3rd rowNew South Wales
4th rowNew South Wales
5th rowNew South Wales
ValueCountFrequency (%)
california 22450
14.1%
british 14116
 
8.9%
columbia 14116
 
8.9%
england 13620
 
8.5%
south 11986
 
7.5%
washington 11264
 
7.1%
new 10432
 
6.5%
wales 10412
 
6.5%
victoria 6016
 
3.8%
seine 5490
 
3.4%
Other values (53) 39532
24.8%
2024-09-11T18:13:16.344176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 132348
 
11.0%
i 128792
 
10.7%
n 113626
 
9.4%
o 77648
 
6.5%
l 75018
 
6.2%
e 71022
 
5.9%
r 66640
 
5.5%
s 58566
 
4.9%
t 51700
 
4.3%
46398
 
3.9%
Other values (41) 381554
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1203312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 132348
 
11.0%
i 128792
 
10.7%
n 113626
 
9.4%
o 77648
 
6.5%
l 75018
 
6.2%
e 71022
 
5.9%
r 66640
 
5.5%
s 58566
 
4.9%
t 51700
 
4.3%
46398
 
3.9%
Other values (41) 381554
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1203312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 132348
 
11.0%
i 128792
 
10.7%
n 113626
 
9.4%
o 77648
 
6.5%
l 75018
 
6.2%
e 71022
 
5.9%
r 66640
 
5.5%
s 58566
 
4.9%
t 51700
 
4.3%
46398
 
3.9%
Other values (41) 381554
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1203312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 132348
 
11.0%
i 128792
 
10.7%
n 113626
 
9.4%
o 77648
 
6.5%
l 75018
 
6.2%
e 71022
 
5.9%
r 66640
 
5.5%
s 58566
 
4.9%
t 51700
 
4.3%
46398
 
3.9%
Other values (41) 381554
31.7%

Product_Category
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
Accessories
70120 
Bikes
25982 
Clothing
16934 

Length

Max length11
Median length11
Mean length9.1714321
Min length5

Characters and Unicode

Total characters1036702
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccessories
2nd rowAccessories
3rd rowAccessories
4th rowAccessories
5th rowAccessories

Common Values

ValueCountFrequency (%)
Accessories 70120
62.0%
Bikes 25982
 
23.0%
Clothing 16934
 
15.0%

Length

2024-09-11T18:13:16.753855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T18:13:17.069258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
accessories 70120
62.0%
bikes 25982
 
23.0%
clothing 16934
 
15.0%

Most occurring characters

ValueCountFrequency (%)
s 236342
22.8%
e 166222
16.0%
c 140240
13.5%
i 113036
10.9%
o 87054
 
8.4%
A 70120
 
6.8%
r 70120
 
6.8%
B 25982
 
2.5%
k 25982
 
2.5%
C 16934
 
1.6%
Other values (5) 84670
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1036702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 236342
22.8%
e 166222
16.0%
c 140240
13.5%
i 113036
10.9%
o 87054
 
8.4%
A 70120
 
6.8%
r 70120
 
6.8%
B 25982
 
2.5%
k 25982
 
2.5%
C 16934
 
1.6%
Other values (5) 84670
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1036702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 236342
22.8%
e 166222
16.0%
c 140240
13.5%
i 113036
10.9%
o 87054
 
8.4%
A 70120
 
6.8%
r 70120
 
6.8%
B 25982
 
2.5%
k 25982
 
2.5%
C 16934
 
1.6%
Other values (5) 84670
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1036702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 236342
22.8%
e 166222
16.0%
c 140240
13.5%
i 113036
10.9%
o 87054
 
8.4%
A 70120
 
6.8%
r 70120
 
6.8%
B 25982
 
2.5%
k 25982
 
2.5%
C 16934
 
1.6%
Other values (5) 84670
 
8.2%

Sub_Category
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Tires and Tubes
33870 
Bottles and Cages
15876 
Road Bikes
13430 
Helmets
12158 
Mountain Bikes
8854 
Other values (12)
28848 

Length

Max length17
Median length15
Mean length11.852649
Min length4

Characters and Unicode

Total characters1339776
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBike Racks
2nd rowBike Racks
3rd rowBike Racks
4th rowBike Racks
5th rowBike Racks

Common Values

ValueCountFrequency (%)
Tires and Tubes 33870
30.0%
Bottles and Cages 15876
14.0%
Road Bikes 13430
 
11.9%
Helmets 12158
 
10.8%
Mountain Bikes 8854
 
7.8%
Jerseys 6010
 
5.3%
Caps 4358
 
3.9%
Fenders 4032
 
3.6%
Touring Bikes 3698
 
3.3%
Gloves 2686
 
2.4%
Other values (7) 8064
 
7.1%

Length

2024-09-11T18:13:17.354136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 49746
20.7%
tires 33870
14.1%
tubes 33870
14.1%
bikes 25982
10.8%
bottles 15876
 
6.6%
cages 15876
 
6.6%
road 13430
 
5.6%
helmets 12158
 
5.0%
mountain 8854
 
3.7%
jerseys 6010
 
2.5%
Other values (13) 25220
10.5%

Most occurring characters

ValueCountFrequency (%)
e 178176
13.3%
s 169756
12.7%
127856
 
9.5%
a 97782
 
7.3%
n 78776
 
5.9%
i 74786
 
5.6%
T 71438
 
5.3%
d 68998
 
5.1%
t 57312
 
4.3%
r 52540
 
3.9%
Other values (23) 362356
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1339776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 178176
13.3%
s 169756
12.7%
127856
 
9.5%
a 97782
 
7.3%
n 78776
 
5.9%
i 74786
 
5.6%
T 71438
 
5.3%
d 68998
 
5.1%
t 57312
 
4.3%
r 52540
 
3.9%
Other values (23) 362356
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1339776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 178176
13.3%
s 169756
12.7%
127856
 
9.5%
a 97782
 
7.3%
n 78776
 
5.9%
i 74786
 
5.6%
T 71438
 
5.3%
d 68998
 
5.1%
t 57312
 
4.3%
r 52540
 
3.9%
Other values (23) 362356
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1339776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 178176
13.3%
s 169756
12.7%
127856
 
9.5%
a 97782
 
7.3%
n 78776
 
5.9%
i 74786
 
5.6%
T 71438
 
5.3%
d 68998
 
5.1%
t 57312
 
4.3%
r 52540
 
3.9%
Other values (23) 362356
27.0%
Distinct130
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2024-09-11T18:13:18.015406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length31
Median length26
Mean length19.500301
Min length12

Characters and Unicode

Total characters2204236
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHitch Rack - 4-Bike
2nd rowHitch Rack - 4-Bike
3rd rowHitch Rack - 4-Bike
4th rowHitch Rack - 4-Bike
5th rowHitch Rack - 4-Bike
ValueCountFrequency (%)
tire 23454
 
6.2%
mountain 19108
 
5.1%
18558
 
4.9%
bottle 15878
 
4.2%
tube 14694
 
3.9%
black 12672
 
3.4%
helmet 12160
 
3.2%
sport-100 12160
 
3.2%
oz 12126
 
3.2%
road 10904
 
2.9%
Other values (71) 225232
59.8%
2024-09-11T18:13:19.111625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
263910
 
12.0%
e 185490
 
8.4%
o 138060
 
6.3%
t 138040
 
6.3%
a 119102
 
5.4%
i 88934
 
4.0%
0 81764
 
3.7%
r 80274
 
3.6%
l 80122
 
3.6%
n 78616
 
3.6%
Other values (47) 949924
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2204236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
263910
 
12.0%
e 185490
 
8.4%
o 138060
 
6.3%
t 138040
 
6.3%
a 119102
 
5.4%
i 88934
 
4.0%
0 81764
 
3.7%
r 80274
 
3.6%
l 80122
 
3.6%
n 78616
 
3.6%
Other values (47) 949924
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2204236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
263910
 
12.0%
e 185490
 
8.4%
o 138060
 
6.3%
t 138040
 
6.3%
a 119102
 
5.4%
i 88934
 
4.0%
0 81764
 
3.7%
r 80274
 
3.6%
l 80122
 
3.6%
n 78616
 
3.6%
Other values (47) 949924
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2204236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
263910
 
12.0%
e 185490
 
8.4%
o 138060
 
6.3%
t 138040
 
6.3%
a 119102
 
5.4%
i 88934
 
4.0%
0 81764
 
3.7%
r 80274
 
3.6%
l 80122
 
3.6%
n 78616
 
3.6%
Other values (47) 949924
43.1%

Order_Quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.90166
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:19.421848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q320
95-th percentile28
Maximum32
Range31
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.5618568
Coefficient of variation (CV)0.80340533
Kurtosis-1.2318761
Mean11.90166
Median Absolute Deviation (MAD)8
Skewness0.37817927
Sum1345316
Variance91.429105
MonotonicityNot monotonic
2024-09-11T18:13:19.765543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 22626
20.0%
2 7650
 
6.8%
3 5142
 
4.5%
4 3436
 
3.0%
6 3076
 
2.7%
5 3039
 
2.7%
18 3010
 
2.7%
7 2993
 
2.6%
24 2983
 
2.6%
16 2978
 
2.6%
Other values (22) 56103
49.6%
ValueCountFrequency (%)
1 22626
20.0%
2 7650
 
6.8%
3 5142
 
4.5%
4 3436
 
3.0%
5 3039
 
2.7%
6 3076
 
2.7%
7 2993
 
2.6%
8 2894
 
2.6%
9 2948
 
2.6%
10 2941
 
2.6%
ValueCountFrequency (%)
32 258
 
0.2%
31 477
 
0.4%
30 2109
1.9%
29 2339
2.1%
28 2533
2.2%
27 2769
2.4%
26 2868
2.5%
25 2736
2.4%
24 2983
2.6%
23 2703
2.4%

Unit_Cost
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.29637
Minimum1
Maximum2171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:20.149618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q342
95-th percentile1555
Maximum2171
Range2170
Interquartile range (IQR)40

Descriptive statistics

Standard deviation549.83548
Coefficient of variation (CV)2.0570257
Kurtosis3.3382506
Mean267.29637
Median Absolute Deviation (MAD)7
Skewness2.1115484
Sum30214112
Variance302319.06
MonotonicityNot monotonic
2024-09-11T18:13:20.496630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2 21312
18.9%
1 14592
12.9%
13 13960
12.4%
8 5718
 
5.1%
3 5562
 
4.9%
9 4920
 
4.4%
7 4358
 
3.9%
2171 3290
 
2.9%
38 3222
 
2.9%
1252 3126
 
2.8%
Other values (24) 32976
29.2%
ValueCountFrequency (%)
1 14592
12.9%
2 21312
18.9%
3 5562
 
4.9%
4 2444
 
2.2%
7 4358
 
3.9%
8 5718
 
5.1%
9 4920
 
4.4%
11 2030
 
1.8%
12 1006
 
0.9%
13 13960
12.4%
ValueCountFrequency (%)
2171 3290
2.9%
1912 262
 
0.2%
1898 286
 
0.3%
1555 1862
1.6%
1519 648
 
0.6%
1482 2036
1.8%
1266 3018
2.7%
1252 3126
2.8%
1083 1634
1.4%
755 702
 
0.6%

Unit_Price
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.93843
Minimum2
Maximum3578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:20.826404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q15
median24
Q370
95-th percentile2443
Maximum3578
Range3576
Interquartile range (IQR)65

Descriptive statistics

Standard deviation922.07122
Coefficient of variation (CV)2.035754
Kurtosis3.1499843
Mean452.93843
Median Absolute Deviation (MAD)19
Skewness2.0880414
Sum51198348
Variance850215.33
MonotonicityNot monotonic
2024-09-11T18:13:21.494682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5 21312
18.9%
35 13960
 
12.4%
2 10416
 
9.2%
9 8116
 
7.2%
4 4176
 
3.7%
22 4032
 
3.6%
3578 3290
 
2.9%
50 3222
 
2.9%
2295 3126
 
2.8%
2320 3018
 
2.7%
Other values (26) 38368
33.9%
ValueCountFrequency (%)
2 10416
9.2%
4 4176
 
3.7%
5 21312
18.9%
8 1804
 
1.6%
9 8116
 
7.2%
10 2444
 
2.2%
21 1686
 
1.5%
22 4032
 
3.6%
24 2682
 
2.4%
25 2238
 
2.0%
ValueCountFrequency (%)
3578 3290
2.9%
3400 262
 
0.2%
3375 286
 
0.3%
2443 2510
2.2%
2384 2036
1.8%
2320 3018
2.7%
2295 3126
2.8%
1701 1634
1.4%
1215 702
 
0.6%
1120 2156
1.9%

Profit
Real number (ℝ)

HIGH CORRELATION 

Distinct1256
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.05166
Minimum-30
Maximum15096
Zeros424
Zeros (%)0.4%
Negative58
Negative (%)0.1%
Memory size883.2 KiB
2024-09-11T18:13:21.923075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile5
Q129
median101
Q3358
95-th percentile1031
Maximum15096
Range15126
Interquartile range (IQR)329

Descriptive statistics

Standard deviation453.88744
Coefficient of variation (CV)1.5922989
Kurtosis35.37105
Mean285.05166
Median Absolute Deviation (MAD)90
Skewness4.0026618
Sum32221100
Variance206013.81
MonotonicityNot monotonic
2024-09-11T18:13:22.354741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1493
 
1.3%
6 1434
 
1.3%
15 1373
 
1.2%
12 1331
 
1.2%
17 1232
 
1.1%
2 1220
 
1.1%
9 1173
 
1.0%
11 1147
 
1.0%
21 1100
 
1.0%
14 1066
 
0.9%
Other values (1246) 100467
88.9%
ValueCountFrequency (%)
-30 1
 
< 0.1%
-29 1
 
< 0.1%
-25 2
< 0.1%
-24 3
< 0.1%
-22 1
 
< 0.1%
-19 1
 
< 0.1%
-18 1
 
< 0.1%
-16 1
 
< 0.1%
-13 1
 
< 0.1%
-12 2
< 0.1%
ValueCountFrequency (%)
15096 1
 
< 0.1%
14055 1
 
< 0.1%
5638 1
 
< 0.1%
5628 1
 
< 0.1%
5485 7
 
< 0.1%
5342 18
< 0.1%
5056 5
 
< 0.1%
5000 1
 
< 0.1%
4626 2
 
< 0.1%
4229 1
 
< 0.1%

Cost
Real number (ℝ)

HIGH CORRELATION 

Distinct360
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.31869
Minimum1
Maximum42978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:22.785683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median108
Q3432
95-th percentile2171
Maximum42978
Range42977
Interquartile range (IQR)404

Descriptive statistics

Standard deviation884.86612
Coefficient of variation (CV)1.885427
Kurtosis97.508784
Mean469.31869
Median Absolute Deviation (MAD)96
Skewness5.0832393
Sum53049908
Variance782988.05
MonotonicityNot monotonic
2024-09-11T18:13:23.110408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1252 2319
 
2.1%
1266 2265
 
2.0%
2171 2242
 
2.0%
26 1757
 
1.6%
24 1698
 
1.5%
1482 1687
 
1.5%
344 1622
 
1.4%
8 1619
 
1.4%
12 1548
 
1.4%
18 1499
 
1.3%
Other values (350) 94780
83.8%
ValueCountFrequency (%)
1 640
 
0.6%
2 1438
1.3%
3 716
0.6%
4 1320
1.2%
5 541
 
0.5%
6 1369
1.2%
7 714
0.6%
8 1619
1.4%
9 881
0.8%
10 1223
1.1%
ValueCountFrequency (%)
42978 1
 
< 0.1%
40014 1
 
< 0.1%
8684 65
 
0.1%
7648 2
 
< 0.1%
7592 5
 
< 0.1%
6513 258
0.2%
6220 57
 
0.1%
6076 20
 
< 0.1%
5736 28
 
< 0.1%
5694 17
 
< 0.1%

Revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct1876
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean754.37036
Minimum2
Maximum58074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.2 KiB
2024-09-11T18:13:23.444299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q163
median223
Q3800
95-th percentile3113
Maximum58074
Range58072
Interquartile range (IQR)737

Descriptive statistics

Standard deviation1309.0947
Coefficient of variation (CV)1.7353474
Kurtosis72.371545
Mean754.37036
Median Absolute Deviation (MAD)200
Skewness4.6709078
Sum85271008
Variance1713728.9
MonotonicityNot monotonic
2024-09-11T18:13:23.867388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 824
 
0.7%
20 783
 
0.7%
10 763
 
0.7%
5 754
 
0.7%
30 752
 
0.7%
34 738
 
0.7%
59 697
 
0.6%
39 673
 
0.6%
8 672
 
0.6%
69 671
 
0.6%
Other values (1866) 105709
93.5%
ValueCountFrequency (%)
2 446
0.4%
3 261
 
0.2%
4 653
0.6%
5 754
0.7%
6 278
 
0.2%
7 306
0.3%
8 672
0.6%
9 511
0.5%
10 763
0.7%
11 138
 
0.1%
ValueCountFrequency (%)
58074 1
 
< 0.1%
54069 1
 
< 0.1%
14312 1
 
< 0.1%
14169 7
 
< 0.1%
14026 18
< 0.1%
13740 5
 
< 0.1%
13310 2
 
< 0.1%
13230 1
 
< 0.1%
12648 1
 
< 0.1%
12451 8
< 0.1%

Interactions

2024-09-11T18:13:04.649340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:42.971567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:45.796033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:48.514188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:51.322480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:53.921449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:56.462358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:59.340358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:02.083167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:04.948571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:43.307796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:46.139005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:48.998638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:51.589657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:54.204481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:56.971954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:59.636532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:02.359187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:05.227824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:43.570385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:46.437130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:49.260386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:51.869144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:54.505589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:57.228523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:59.951629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:02.641035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:05.507677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:43.900392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:46.762652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:49.534998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:52.187809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:54.800549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:57.523090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:00.280614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:02.951590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:05.854546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:44.186743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:47.065495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:49.850999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:52.479911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:55.091208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:57.829657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:00.599600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:03.208302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:06.151187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:44.469278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:47.294701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:50.182760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:52.758737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:55.316276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:58.107633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:00.900193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:03.455133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:06.712768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:44.822189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:47.557563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:50.497681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:53.076039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:55.568965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:58.424646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:01.215382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:03.761862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:07.036144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:45.156353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:47.905558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:50.793293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:53.367920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:55.875441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:58.743087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:01.484348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:04.075056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:07.299248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:45.484852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:48.207120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:51.079649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:53.613435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:56.144467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:12:59.070417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:01.764087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-11T18:13:04.342523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-11T18:13:24.184993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Age_GroupCostCountryCustomer_AgeCustomer_GenderDayMonthOrder_QuantityProduct_CategoryProfitRevenueSub_CategoryUnit_CostUnit_PriceYear
Age_Group1.0000.0040.0690.8920.0240.0160.0530.0220.0430.0050.0050.0480.0400.0380.042
Cost0.0041.0000.0190.0150.0050.0020.008-0.1970.1590.8950.9890.1610.9050.901-0.153
Country0.0690.0191.0000.0720.0240.0210.0330.0450.0960.0180.0140.0820.0820.0730.031
Customer_Age0.8920.0150.0721.0000.025-0.0150.0390.0210.0630.0250.0200.0400.0140.0140.041
Customer_Gender0.0240.0050.0240.0251.0000.0170.0290.0130.0120.0080.0050.0200.0340.0270.026
Day0.0160.0020.021-0.0150.0171.0000.043-0.0020.0100.0040.0020.0130.0040.004-0.007
Month0.0530.0080.0330.0390.0290.0431.0000.0190.0460.0090.0080.0270.0270.0300.394
Order_Quantity0.022-0.1970.0450.0210.013-0.0020.0191.0000.534-0.125-0.1670.255-0.509-0.5080.103
Product_Category0.0430.1590.0960.0630.0120.0100.0460.5341.0000.2100.1671.0000.7070.7070.295
Profit0.0050.8950.0180.0250.0080.0040.009-0.1250.2101.0000.9440.1570.8000.816-0.135
Revenue0.0050.9890.0140.0200.0050.0020.008-0.1670.1670.9441.0000.1450.8900.892-0.149
Sub_Category0.0480.1610.0820.0400.0200.0130.0270.2551.0000.1570.1451.0000.4980.4790.220
Unit_Cost0.0400.9050.0820.0140.0340.0040.027-0.5090.7070.8000.8900.4981.0000.997-0.145
Unit_Price0.0380.9010.0730.0140.0270.0040.030-0.5080.7070.8160.8920.4790.9971.000-0.145
Year0.042-0.1530.0310.0410.026-0.0070.3940.1030.295-0.135-0.1490.220-0.145-0.1451.000

Missing values

2024-09-11T18:13:07.769320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-11T18:13:08.827151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateDayMonthYearCustomer_AgeAge_GroupCustomer_GenderCountryStateProduct_CategorySub_CategoryProductOrder_QuantityUnit_CostUnit_PriceProfitCostRevenue
02013-11-2626November201319Youth (<25)MCanadaBritish ColumbiaAccessoriesBike RacksHitch Rack - 4-Bike845120590360950
12015-11-2626November201519Youth (<25)MCanadaBritish ColumbiaAccessoriesBike RacksHitch Rack - 4-Bike845120590360950
22014-03-2323March201449Adults (35-64)MAustraliaNew South WalesAccessoriesBike RacksHitch Rack - 4-Bike2345120136610352401
32016-03-2323March201649Adults (35-64)MAustraliaNew South WalesAccessoriesBike RacksHitch Rack - 4-Bike204512011889002088
42014-05-1515May201447Adults (35-64)FAustraliaNew South WalesAccessoriesBike RacksHitch Rack - 4-Bike445120238180418
52016-05-1515May201647Adults (35-64)FAustraliaNew South WalesAccessoriesBike RacksHitch Rack - 4-Bike545120297225522
62014-05-2222May201447Adults (35-64)FAustraliaVictoriaAccessoriesBike RacksHitch Rack - 4-Bike445120199180379
72016-05-2222May201647Adults (35-64)FAustraliaVictoriaAccessoriesBike RacksHitch Rack - 4-Bike24512010090190
82014-02-2222February201435Adults (35-64)MAustraliaVictoriaAccessoriesBike RacksHitch Rack - 4-Bike224512010969902086
92016-02-2222February201635Adults (35-64)MAustraliaVictoriaAccessoriesBike RacksHitch Rack - 4-Bike214512010469451991
DateDayMonthYearCustomer_AgeAge_GroupCustomer_GenderCountryStateProduct_CategorySub_CategoryProductOrder_QuantityUnit_CostUnit_PriceProfitCostRevenue
1130262013-07-088July201329Young Adults (25-34)MGermanyHessenClothingVestsClassic Vest, L2024647104801190
1130272015-07-088July201529Young Adults (25-34)MGermanyHessenClothingVestsClassic Vest, L2124647465041250
1130282013-12-2828December201341Adults (35-64)MUnited KingdomEnglandClothingVestsClassic Vest, S224647548123
1130292015-12-2828December201541Adults (35-64)MUnited KingdomEnglandClothingVestsClassic Vest, S224647548123
1130302014-04-1212April201441Adults (35-64)MUnited KingdomEnglandClothingVestsClassic Vest, S62464225144369
1130312016-04-1212April201641Adults (35-64)MUnited KingdomEnglandClothingVestsClassic Vest, S3246411272184
1130322014-04-022April201418Youth (<25)MAustraliaQueenslandClothingVestsClassic Vest, M2224646555281183
1130332016-04-022April201618Youth (<25)MAustraliaQueenslandClothingVestsClassic Vest, M2224646555281183
1130342014-03-044March201437Adults (35-64)FFranceSeine (Paris)ClothingVestsClassic Vest, L2424646845761260
1130352016-03-044March201637Adults (35-64)FFranceSeine (Paris)ClothingVestsClassic Vest, L2324646555521207

Duplicate rows

Most frequently occurring

DateDayMonthYearCustomer_AgeAge_GroupCustomer_GenderCountryStateProduct_CategorySub_CategoryProductOrder_QuantityUnit_CostUnit_PriceProfitCostRevenue# duplicates
172012-11-2727November201227Young Adults (25-34)MCanadaBritish ColumbiaBikesRoad BikesRoad-650 Red, 44248778357697415503
992013-09-1515September201327Young Adults (25-34)FCanadaBritish ColumbiaAccessoriesTires and TubesPatch Kit/8 Patches16121616323
1682013-11-2222November201331Young Adults (25-34)FCanadaBritish ColumbiaAccessoriesBottles and CagesWater Bottle - 30 oz.222565441093
3112014-03-1717March201435Adults (35-64)MCanadaBritish ColumbiaAccessoriesBottles and CagesWater Bottle - 30 oz.15254430743
6722015-11-1919November201541Adults (35-64)MUnited StatesCaliforniaAccessoriesTires and TubesPatch Kit/8 Patches18121718353
6802015-11-2424November201541Adults (35-64)MUnited StatesCaliforniaAccessoriesTires and TubesPatch Kit/8 Patches81288163
8382016-03-2929March201633Young Adults (25-34)FCanadaBritish ColumbiaAccessoriesTires and TubesMountain Tire Tube242571481193
02011-02-2020February201125Young Adults (25-34)FCanadaBritish ColumbiaBikesRoad BikesRoad-250 Red, 52115192443900151924192
12011-03-033March201143Adults (35-64)FUnited StatesCaliforniaBikesRoad BikesRoad-150 Red, 441217135781335217135062
22011-03-2424March201124Youth (<25)MAustraliaVictoriaBikesRoad BikesRoad-250 Black, 44115552443375155519302